Consistent temporal variations in many outdoor scenes

Nathan Jacobs; Nathaniel Roman; Robert Pless

doi:10.1109/CVPR.2007.383258

Consistent temporal variations in many outdoor scenes

Nathan Jacobs, Nathaniel Roman, Robert Pless

Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

144 Scopus citations

Abstract

This paper details an empirical study of large image sets taken by static cameras. These images have consistent correlations over the entire image and over time scales of days to months. Simple second-order statistics of such image sets show vastly more structure than exists in generic natural images or video from moving cameras. Using a slight variant to PCA, we can decompose all cameras into comparable components and annotate images with respect to surface orientation, weather, and seasonal change. Experiments are based on a data set from 538 cameras across the United States which have collected more than 17 million images over the the last 6 months.

Original language	English
Title of host publication	2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
DOIs	https://doi.org/10.1109/CVPR.2007.383258
State	Published - 2007
Event	2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 - Minneapolis, MN, United States Duration: Jun 17 2007 → Jun 22 2007

Publication series

Name	Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)	1063-6919

Conference

Conference	2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
Country/Territory	United States
City	Minneapolis, MN
Period	6/17/07 → 6/22/07

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition

Access to Document

10.1109/CVPR.2007.383258

Cite this

@inproceedings{308e25461ab34f169fec261b13d3ab9c,

title = "Consistent temporal variations in many outdoor scenes",

abstract = "This paper details an empirical study of large image sets taken by static cameras. These images have consistent correlations over the entire image and over time scales of days to months. Simple second-order statistics of such image sets show vastly more structure than exists in generic natural images or video from moving cameras. Using a slight variant to PCA, we can decompose all cameras into comparable components and annotate images with respect to surface orientation, weather, and seasonal change. Experiments are based on a data set from 538 cameras across the United States which have collected more than 17 million images over the the last 6 months.",

author = "Nathan Jacobs and Nathaniel Roman and Robert Pless",

year = "2007",

doi = "10.1109/CVPR.2007.383258",

language = "English",

isbn = "1424411807",

series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",

booktitle = "2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07",

note = "2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07 ; Conference date: 17-06-2007 Through 22-06-2007",

}

Jacobs, N, Roman, N & Pless, R 2007, Consistent temporal variations in many outdoor scenes. in 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07., 4270283, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07, Minneapolis, MN, United States, 6/17/07. https://doi.org/10.1109/CVPR.2007.383258

Consistent temporal variations in many outdoor scenes. / Jacobs, Nathan; Roman, Nathaniel; Pless, Robert.
2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07. 2007. 4270283 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - This paper details an empirical study of large image sets taken by static cameras. These images have consistent correlations over the entire image and over time scales of days to months. Simple second-order statistics of such image sets show vastly more structure than exists in generic natural images or video from moving cameras. Using a slight variant to PCA, we can decompose all cameras into comparable components and annotate images with respect to surface orientation, weather, and seasonal change. Experiments are based on a data set from 538 cameras across the United States which have collected more than 17 million images over the the last 6 months.

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Consistent temporal variations in many outdoor scenes

Abstract

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